| Citation: | HUANG Junjie, ZHU Jianqin, CHENG Zeyuan. Three-dimensional thermal stress prediction method in double-wall structure using convolutional neural networks[J]. Journal of Aerospace Power, 2023, 38（7）：1658-1667 doi: 10.13224/j.cnki.jasp.20220754
|
To realize the prediction of 3D physical field, a method for rapid evaluation of 3D thermal stress on the outer wall of double-wall cooling structure using convolutional neural network (CNN) was presented. For the structural characteristics of the flat plate shape of the double-wall cooling structure outer wall, the temperature field was sliced into multiple sections along the wall thickness direction. The temperature was used as the basic element of the input tensor of the CNN, and the sections at different thickness positions corresponded to channel dimensions of the input tensor. The 3D temperature field was input into the CNN for output of the 3D von-Mises stress field under thermal load. The average absolute error of the trained converged network on the testing set was 1.23 MPa, with an average relative error of 15.10%, and the average absolute error for peak stresses was 16.10 MPa, with an average relative error of 11.81%. Results showed that for the thermal stress prediction problem of double-wall cooling structures, CNN can complete the temperature-to-stress mapping well. Exploring potential mechanisms of the thermoelasticity problems by using deep learning methods is expected to be realized.
| [1] |
CHANG S W,CHIANG P A,CAI W L. Thermal performance of impinging jet-row onto trapezoidal channel with different effusion and discharge conditions[J]. International Journal of Thermal Sciences,2021,159: 106590.1-106590.21.
|
| [2] |
LIU Yuyang,RAO Yu,YANG Li,et al. Flow and heat transfer characteristics of double-wall cooling with multi-row short film cooling hole arrangements[J]. International Journal of Thermal Sciences,2021,165: 106878.1-106878.13.
|
| [3] |
MOON M A,KIM K Y. Analysis and optimization of fan-shaped pin-fin in a rectangular cooling channel[J]. International Journal of Heat and Mass Transfer,2014,72: 148-162. doi: 10.1016/j.ijheatmasstransfer.2013.12.085
|
| [4] |
WANG S,ZHANG Z. Failure mechanism of turbine guide vane and oxide composition analysis on the surface of failure vane cracks[J]. Engineering Failure Analysis,2020,117: 104763.1-104763.8.
|
| [5] |
GUO Zixu,SONG Ziyuan,FAN Jun,et al. Experimental and analytical investigation on service life of film cooling structure for single crystal turbine blade[J]. International Journal of Fatigue,2021,150: 106318.1-106318.13.
|
| [6] |
KIM K M,MOON H,PARK J S,et al. Optimal design of impinging jets in an impingement/effusion cooling system[J]. Energy,2014,66: 839-848. doi: 10.1016/j.energy.2013.12.024
|
| [7] |
ELMUKASHFI E,MURRAY A V,IRELAND P T,et al. Analysis of the thermomechanical stresses in double-wall effusion cooled systems[J]. Journal of Turbomachinery,2020,142(5): 051002.1-051002.13.
|
| [8] |
SKAMNIOTIS C,COCKS A C F. Minimizing stresses in double wall transpiration cooled components for high temperature applications[J]. International Journal of Mechanical Sciences,2021,189: 105983.1-105983.13.
|
| [9] |
GUO Zixu,HUANG Dawei,YAN Xiaojun,et al. A damage coupled elastic-plastic constitutive model and its application on low cycle fatigue life prediction of turbine blade[J]. International Journal of Fatigue,2020,131: 105298.1-105298.12.
|
| [10] |
HONG Hui,WANG Weizhe,LIU Yingzheng. High-temperature fatigue behavior of a steam turbine rotor under flexible operating conditions with variable loading amplitudes[J]. International Journal of Mechanical Sciences,2019,163: 105121.1-105121.13.
|
| [11] |
GIANNELLA V,VIVO E,MAZZEO M,et al. FEM-DBEM approach to simulate crack propagation in a turbine vane segment undergoing a fatigue load spectrum[J]. Procedia Structural Integrity,2018,12: 479-491. doi: 10.1016/j.prostr.2018.11.070
|
| [12] |
CUNHA F J,DAHMER M T,CHYU M K. Thermal-mechanical life prediction system for anisotropic turbine components[J]. Journal of Turbomachinery,2006,128(2): 240-250. doi: 10.1115/1.2137740
|
| [13] |
BANASZKIEWICZ M. Numerical investigations of crack initiation in impulse steam turbine rotors subject to thermo-mechanical fatigue[J]. Applied Thermal Engineering,2018,138: 761-773. doi: 10.1016/j.applthermaleng.2018.04.099
|
| [14] |
MURRAY A V, IRELAND P T, RAWLINSON A J. An integrated conjugate computational approach for evaluating the aerothermal and thermomechanical performance of double-wall effusion cooled systems[R]. ASME Paper GT2017-64711, 2017.
|
| [15] |
RONNEBERGER O, FISCHER P, BROX T. U-net: convolutional networks for biomedical image segmentation[C]//Proceedings of the 18th Medical Image Computing and Computer-Assisted Intervention（MICCAI) International Conference. Munich, Germany: Springer, 2015: 234-241.
|
| [16] |
HU Jie, SHEN Li, SUN Gang. Squeeze-and-excitation networks[C]//Proceedings of the IEEE conference on Computer Vision and Pattern Recognition. Salt Lake City, US: IEEE, 2018: 7132-7141.
|
| [17] |
LIU Peirong, TIAN Lin, ZHANG Yubo, et al. Discovering hidden physics behind transport dynamics[C]//Proceedings of the Institute of Electrical and Electronics Engineers/ Computer Vision Foundation (IEEE/CVF) Conference on Computer Vision and Pattern Recognition. Nashville, US: IEEE, 2021: 10082-10092.
|
| [18] |
BADARINATH P V,CHIERICHETTI M,KAKHKI F D. A machine learning approach as a surrogate for a finite element analysis: status of research and application to one dimensional system[J]. Sensors,2021,21(5): 1654.1-1654.18.
|
| [19] |
LIANG L,LIU M,MARTIN C,et al. A deep learning approach to estimate stress distribution: a fast and accurate surrogate of finite-element analysis[J]. Journal of The Royal Society Interface,2018,15(138): 20170844.1-20170844.10.
|
| [20] |
HERATH S,HAPUTHANTHRI U. Topologically optimal design and failure prediction using conditional generative adversarial networks[J]. International Journal for Numerical Methods in Engineering,2021,122(23): 6867-6887. doi: 10.1002/nme.6814
|
| [21] |
YANG Zhenze,YU Chihua,BUEHLER M J. Deep learning model to predict complex stress and strain fields in hierarchical composites[J]. Science Advances,2021,7(15): 7416.1-7416.11.
|
| [22] |
NING L,CAI Z,LIU Y,et al. Conditional generative adversarial network driven approach for direct prediction of thermal stress based on two-phase material SEM images[J]. Ceramics International,2021,47(24): 34115-34126. doi: 10.1016/j.ceramint.2021.08.322
|
DownLoad:
